Are you confident you understand AI pair programming tools’ impact across your teams? Many engineering leaders have taken a leap of faith to pilot the technology, but are now looking for more substantial proof they can realize the value.
In this case study, an engineering leader harnesses holistic engineering metrics to measure the ROI of AI pair programming with GitHub Copilot.
Read this GitHub Copilot case study to learn how to:
- Evaluate GitHub Copilot’s impact with engineering productivity data
- Observe whether the benefits of GitHub Copilot justify the investment
- Identify the right training, process, and workflow improvements needed to support changing behaviors and shifting bottlenecks
- Align expectations with executive leadership around development productivity with AI
Download the case study today
Take a 5-minute tour of the Copilot Evaluation dashboards used in this case study
Faros AI is a SaaS solution that’s quick and easy to set up to start your evaluation of AI pair programming effectiveness. Faros AI expertly ingests telemetry from coding assistants, responses from developer surveys, and additional data from SDLC tools to evaluate AI pair programming tools like GitHub Copilot, Amazon Q Developer (formally Amazon CodeWhisperer), and more. Dashboards light up instantly to illustrate the impact on engineering productivity and developer satisfaction.
